At Instructure, we believe in the power of people to grow and succeed throughout their lives. Our goal is to amplify that power by creating intuitive products that simplify learning and personal development, facilitate meaningful relationships, and inspire people to go further in their education and careers.
We do this by giving smart, creative, passionate people opportunities to create awesome. And that's where you come in:
Our AI team is where a lot of that gets built: applying advanced AI to real problems in learning, and turning research into product capabilities that educators and students use every day.
We're looking for a Senior Machine Learning Engineer to build and own the learner models behind our mastery and progression capabilities. You'll design the models, build the pipelines that train and score them, and own their quality once they're running in production.
You'll partner with our learning scientists on what these models should measure, and with our infrastructure team on deployment and operations.
What You'll DoDesign and build learner models, including knowledge tracing and longitudinal approaches, that power mastery and progression features surfaced to learners and educators
Shape the data foundation for learner modeling: define which signals matter, and build the datasets your models depend on
Translate mastery and progression definitions into model targets and evaluation criteria, working with learning scientists and product partners
Build estimation and scoring approaches that hold up on sparse, noisy, and evolving behavioral data
Own your models in production: training and scoring pipelines, testing, versioning, and monitoring quality once they're live
Explain model behavior, assumptions, and limitations clearly to product, engineering, and learning partners
Six or more years in applied machine learning, machine learning engineering, or applied research, with ownership of models shipped into real products
Depth in at least one of: sequence modeling, latent-variable or probabilistic modeling, temporal modeling, Bayesian methods, or calibration of model outputs, applied to data that changes over time
Strong Python and production engineering skills: you write the pipelines that train and score your models, and you've shipped models that run on a schedule and serve predictions to real users
Strong evaluation instincts around calibration, uncertainty, stability, fairness, interpretability, and validation strategy
Experience with recommender systems, user-state modeling, or personalization at scale
Experience with knowledge tracing, psychometrics, educational measurement, or adaptive learning systems
Experience combining structured knowledge representations, such as skills, standards, or concept graphs, with learner models
Experience designing experiments or observational validation strategies to test whether a model reflects reality
Onsite Collaboration Requirement: This role requires working onsite on Tuesday and Wednesday, with Thursday strongly encouraged as part of our company’s in-person collaboration model.
Why Join UsJoin us and help shape the future of education by turning cutting-edge AI into reliable product capabilities.
At Instructure, we're on a mission to help educators and students learn together, anytime, anywhere, and however works best. You'll join our research-driven team tackling education's biggest challenges with cutting-edge technology.
We value diversity, creativity, and passion, and invest in our teams through mentorship, hack weeks, internal conferences, and a culture where innovation thrives. Here, you'll have the chance to build the next generation of LMS features that make a real impact on students and teachers, and do it in a collaborative, supportive environment that encourages experimentation and growth.
Get in on all the awesome at Instructure!
We offer competitive, meaningful benefits in every country where we operate. While they vary by location, here's a general idea of what you can expect:
Competitive compensation, plus all full-time employees participate in our ownership program - because everyone should have a stake in our success.
Flexible work culture. Our remote, hybrid and in-office collaboration spaces vary by role, team and location.
Generous time off, including local holidays and our annual “Dim the Lights” period in late December, when teams are encouraged to step back and recharge based on departmental needs.
Comprehensive wellness programs and mental health support
Learning and development resources, including professional development tools and tuition reimbursement, to support your growth
The technology and tools you need to do your best work
Motivosity employee recognition program
A culture rooted in inclusivity, support, and meaningful connection
We believe in hiring great people and treating them right. The more diverse we are, the better our ideas and outcomes.
Instructure is an Equal Opportunity Employer. We comply with applicable employment and anti-discrimination laws in every country where we operate.
All employees must pass a background check as part of the hiring process. To help protect our teams and systems, we’ve implemented identity verification measures. Candidates may be asked to verify their legal name, current physical location, and provide a valid contact number and residential address, in accordance with local data privacy laws.
Any attempt to misrepresent personal or professional information will result in disqualification.
Skills Required
- Six or more years of experience in applied machine learning, machine learning engineering, or applied research
- Experience owning machine learning models shipped into real products
- Depth in sequence modeling, latent-variable or probabilistic modeling, temporal modeling, Bayesian methods, or calibration of model outputs applied to changing data
- Strong Python and production engineering skills
- Experience building pipelines that train and score models on a schedule and serve predictions to users
- Strong evaluation skills involving calibration, uncertainty, stability, fairness, interpretability, and validation strategy
- Experience with recommender systems, user-state modeling, or personalization at scale
- Experience with knowledge tracing, psychometrics, educational measurement, or adaptive learning systems
- Experience combining structured knowledge representations with learner models
- Experience designing experiments or observational validation strategies
Instructure Compensation & Benefits Highlights
The following summarizes recurring compensation and benefits themes identified from responses generated by popular LLMs to common candidate questions about Instructure and has not been reviewed or approved by Instructure.
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Fair & Transparent Compensation — Pay is considered market-competitive for many engineering, product, and quota-carrying roles, especially when factoring base, variable, and equity. In these tracks, total rewards are often characterized as fair-to-strong for the role and location.
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Healthcare Strength — Health coverage is described as comprehensive, including medical, dental, vision, mental-health support, and HSA/FSA options. Employer contributions are often portrayed as strong, and core medical benefits receive consistently positive marks.
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Leave & Time Off Breadth — Time off offerings include flexible or “unlimited” PTO, paid holidays, and paid sick time, paired with widespread remote/hybrid flexibility. This breadth of time off and work flexibility is often viewed as a meaningful perk that enhances overall value.
Instructure Insights
What We Do
Instructure is helping people grow from the first day of school to the last day of work. More than 30 million people use its Canvas and Bridge platforms for learning management and employee development.
